Statistical mediation analysis allows researchers to investigate the mechanisms by which a predictor variable affects an outcome variable. A mediator differs from other third variables such as a confounder or moderator in that the mediator is intermediate in the causal process such that predictor causally influences the mediator, which in turn causally influences the outcome. This presentation will first address the theory and examples of mediators in psychological research, and then implications of the use of mediation analysis in treatment and prevention research. The single mediator model including estimation and model assumptions will be overviewed. Selected topics in estimating mediation including product of coefficients and its distribution, multiple mediator models, longitudinal models of mediation, and latent variable models will then be described by addressing the recent studies in each topic. Furthermore, developments in sensitivity analysis and statistical methods to improve the causal interpretation of mediation relations will be addressed.